TabNet: Attentive Interpretable Tabular Learning

Programmatically Interpretable Reinforcement Learning

We study the problem of generating interpretable and verifiable policies through reinforcement learning. Unlike the popular Deep Reinforcement Learning (DRL) paradigm, in which the policy is represented by a neural network, the aim in Programmatically Interpretable Reinforcement Learning (PIRL) is to find a policy that can be represented in a high-level programming language. Such programmatic p...

Learning interpretable models

Interpretable Active Learning

Active learning has long been a topic of study in machine learning. However, as increasingly complex and opaque models have become standard practice, the process of active learning, too, has become more opaque. There has been little investigation into interpreting what specific trends and patterns an active learning strategy may be exploring. This work expands on the Local Interpretable Model-a...

Sparse Tabular Multiagent Q-learning

Multiagent learning problems can in principle be solved by treating the joint actions of the agents as single actions and applying singleagent Q-learning. However, the number of joint actions is exponential in the number of agents, rendering this approach infeasible for most problems. In this paper we investigate a sparse representation of the Q-function by only considering the joint actions in...

A NOTE TO INTERPRETABLE FUZZY MODELS AND THEIR LEARNING

In this paper we turn the attention to a well developed theory of fuzzy/lin-guis-tic models that are interpretable and, moreover, can be learned from the data.We present four different situations demonstrating both interpretability as well as learning abilities of these models.